CERN Accelerating science

CMS Note
Report number	CMS-CR-2018-406
Title	Operational experience with the new CMS DAQ-Expert
Related title	Operational experience with the new CMS DAQ expert
Author(s)	Sakulin, Hannes (CERN) ; Andre, Jean-Marc (Fermilab) ; Behrens, Ulf (DESY) ; Branson, James (UC, San Diego) ; Brummer, Philipp (CERN ; KIT, Karlsruhe) ; Cittolin, Sergio (UC, San Diego) ; da Silva Gomes, Diego (CERN) ; Darlea, Georgiana-Lavinia (MIT) ; Deldicque, Christian (CERN) ; Demiragli, Zeynep (MIT) ; Dobson, Marc (CERN) ; Doualot, Nicolas (Fermilab) ; Erhan, Samim (UCLA) ; Fulcher, Jonathan Richard (CERN) ; Gigi, Dominique (CERN) ; Gladki, Maciej (CERN) ; Glege, Frank (CERN) ; Gomez-Ceballos, Guillelmo (MIT) ; Hegeman, Jeroen (CERN) ; Holzner, Andre (UC, San Diego) ; Lettrich, Michael (CERN) ; Mecionis, Audrius (Fermilab ; Vilnius U. (main)) ; Meijers, Frans (CERN) ; Meschi, Emilio (CERN) ; Mommsen, Remigius K. (Fermilab) ; Morovic, Srecko (Fermilab) ; O´Dell, Vivian (Fermilab) ; Orsini, Luciano (CERN) ; Papakrivopoulos, Ioannis (Natl. Tech. U., Athens) ; Paus, Christoph (MIT) ; Petrucci, Andrea (Rice U. (main)) ; Pieri, Marco (UC, San Diego) ; Rabady, Dinyar (CERN) ; Racz, Attila (CERN) ; Rapsevicius, Valdas (Fermilab ; Vilnius U. (main)) ; Reis, Thomas (CERN) ; Schwick, Christoph (CERN) ; Simelevicius, Dainius (CERN ; Vilnius U. (main)) ; Stankevicius, Mantas (Fermilab ; Vilnius U. (main)) ; Vazquez Velez, Cristina (CERN) ; Vougioukas, Michail (CERN) ; Wernet, Christian (CERN) ; Zejdl, Petr (Fermilab ; CERN)
Publication	2019
Imprint	03 Dec 2018
Number of pages	8
In:	EPJ Web Conf. 214 (2019) 01015
In:	23rd International Conference on Computing in High Energy and Nuclear Physics, CHEP 2018, Sofia, Bulgaria, 9 - 13 Jul 2018, pp.01015
DOI	10.1051/epjconf/201921401015
Subject category	Detectors and Experimental Techniques
Accelerator/Facility, Experiment	CERN LHC ; CMS
Abstract	The data acquisition (DAQ) system of the Compact Muon Solenoid (CMS) at CERN reads out the detector at the level-1 trigger accept rate of 100 kHz, assembles events with a bandwidth of 200 GB/s, provides these events to the high level-trigger running on a farm of about 30k cores and records the accepted events. Comprising custom-built and cutting edge commercial hardware and several 1000 instances of software applications, the DAQ system is complex in itself and failures cannot be completely excluded. Moreover, problems in the readout of the detectors, in the first level trigger system or in the high level trigger may provoke anomalous behaviour of the DAQ system which sometimes cannot easily be differentiated from a problem in the DAQ system itself. In order to achieve high data taking efficiency with operators from the entire collaboration and without relying too heavily on the on-call experts, an expert system, the DAQ-Expert, has been developed that can pinpoint the source of most failures and give advice to the shift crew on how to recover in the quickest way. The DAQ-Expert constantly analyzes monitoring data from the DAQ system and the high level trigger by making use of logic modules written in Java that encapsulate the expert knowledge about potential operational problems. The results of the reasoning are presented to the operator in a web-based dashboard, may trigger sound alerts in the control room and are archived for post-mortem analysis - presented in a web-based timeline browser. We present the design of the DAQ-Expert and report on the operational experience since 2017, when it was first put into production.
Copyright/License	publication: © 2019-2024 The Authors (License: CC-BY-4.0)

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